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- W4386870334 abstract "Mental stress is a major problem these days, especially among teenagers. Mental health problems affect students’ health. They affect many areas of a student's life, affecting the quality of life, academic performance, physical health and negatively affecting relationships with friends and family. These problems can affect future employment, income potential, overall health, and can also have long-term effects on the students. Early detection can help them before they go into depression and offer remedial measures that might help to relieve stress. Unfortunately, there is no real-time technique for automatic, continuous, consistent, and reliable stress detection at an early stage. We provide a new framework for instantaneous stress detection. This framework detects hu-man stress based on facial expressions, facial cues (stress score), and breathing patterns (respiration rate). Facial expressions of anger, disgust, fear, and sadness are all signs of stress. The distance between eyebrows and lip movement can also be used as stress indicators. Data on breathing patterns is critical for mental health analysis because it can detect early signs of stress and depression. Skeletal tracking with an Intel depth-sensing camera is used to collect this data. Finally, a machine learning model is built on the collected multimodal data to accurately predict stress levels in multiple subjects." @default.
- W4386870334 created "2023-09-20" @default.
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- W4386870334 date "2023-01-01" @default.
- W4386870334 modified "2023-09-27" @default.
- W4386870334 title "Stress Detection Based on Multimodal Data in a Classroom Environment" @default.
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- W4386870334 doi "https://doi.org/10.1007/978-981-99-5080-5_14" @default.
- W4386870334 hasPublicationYear "2023" @default.
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